System and method for analyzing patient information for use in automated patient care

ABSTRACT

One or more physiological measures regularly recorded by an implantable medical device and relating to individual patient information recorded on a substantially continuous basis are retrieved from a patient care record. The physiological measures retrieved from one such patient care record are analyzed to determine a patient status. Each of the physiological measures are representative of at least one of measured and derived patient information.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a continuation of Ser. No. 09/476,602 filedDec. 31, 1999 U.S. Pat. No. 6,270,457, issued Aug. 7, 2001, which is acontinuation-in-part of Ser. No. 09/324,894 filed Jun. 3, 1999 U.S. Pat.No. 6,312,378, issued Nov. 6, 2001, the priority dates of which areclaimed and the disclosures of which are incorporated by reference.

FIELD OF THE INVENTION

The present invention relates in general to automated data collectionand analysis, and, in particular, to a system and method for analyzingpatient information for use in automated patient care.

BACKGROUND OF THE INVENTION

A broad class of medical subspecialties, including cardiology,endocrinology, hematology, neurology, gastroenterology, urology,ophthalmology, and otolaryngology, to name a few, rely on accurate andtimely patient information for use in aiding health care providers indiagnosing and treating diseases and disorders. Often, proper medicaldiagnosis requires information on physiological events of short durationand sudden onset, yet these types of events are often occur infrequentlyand with little or no warning. Fortunately, such patient information canbe obtained via external, implantable, cutaneous, subcutaneous, andmanual medical devices, and combinations thereof. For example, in thearea of cardiology, implantable pulse generators (IPGs) are commonlyused to treat irregular heartbeats, known as arrhythmias. There arethree basic types of IPGs. Cardiac pacemakers are used to managebradycardia, an abnormally slow or irregular heartbeat. Bradycardia cancause symptoms such as fatigue, dizziness, and fainting. Implantablecardioverter defibrillators (ICDs) are used to treat tachycardia, heartrhythms that are abnormally fast and life threatening. Tachycardia canresult in sudden cardiac death (SCD). Finally, implantablecardiovascular monitors and therapeutic devices are used to monitor andtreat structural problems of the heart, such as congestive heart failureand rhythm problems.

Pacemakers and ICDs, as well as other types of implantable and externalmedical devices, are equipped with an on-board, volatile memory in whichtelemetered signals can be stored for later retrieval and analysis. Inaddition, a growing class of cardiac medical devices, includingimplantable heart failure monitors, implantable event monitors,cardiovascular monitors, and therapy devices, are being used to providesimilar stored device information. These devices are able to store morethan thirty minutes of per heartbeat data. Typically, the telemeteredsignals can provide patient device information recorded on a perheartbeat, binned average basis, or derived basis from, for example,atrial electrical activity, ventricular electrical activity, minuteventilation, patient activity score, cardiac output score, mixed venousoxygen score, cardiovascular pressure measures, time of day, and anyinterventions and the relative success of such interventions. Inaddition, many such devices can have multiple sensors, or severaldevices can work together, for monitoring different sites within apatient's body.

Presently, stored device information is retrieved using a proprietaryinterrogator or programmer, often during a clinic visit or following adevice event. The volume of data retrieved from a single deviceinterrogation “snapshot” can be large and proper interpretation andanalysis can require significant physician time and detailedsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists. The sequential logging and analysis of regularlyscheduled interrogations can create an opportunity for recognizingsubtle and incremental changes in patient condition otherwiseundetectable by inspection of a single “snapshot.” However, presentapproaches to data interpretation and understanding and practicallimitations on time and physician availability make such analysisimpracticable.

A prior art system for collecting and analyzing pacemaker and ICDtelemetered signals in a clinical or office setting is the Model 9790Programmer, manufactured by Medtronic, Inc., Minneapolis, Minn. Thisprogrammer can be used to retrieve data, such as patientelectrocardiogram and any measured physiological conditions, collectedby the IPG for recordation, display and printing. The retrieved data isdisplayed in chronological order and analyzed by a physician. Comparableprior art systems are available from other IPG manufacturers, such asthe Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes a removable floppydiskette mechanism for patient data storage. These prior art systemslack remote communications facilities and must be operated with thepatient present. These systems present a limited analysis of thecollected data based on a single device interrogation and lack thecapability to recognize trends in the data spanning multiple episodesover time or relative to a disease specific peer group.

A prior art system for locating and communicating with a remote medicaldevice implanted in an ambulatory patient is disclosed in U.S. Pat. No.5,752,976 ('976). The implanted device includes a telemetry transceiverfor communicating data and operating instructions between the implanteddevice and an external patient communications device. The communicationsdevice includes a communication link to a remote medical supportnetwork, a global positioning satellite receiver, and a patientactivated link for permitting patient initiated communication with themedical support network.

Related prior art systems for remotely communicating with and receivingtelemetered signals from a medical device are disclosed in U.S. Pat. No.5,113,869 ('869) and U.S. Pat. No. 5,336,245 ('245). In the '869 patent,an implanted AECG monitor can be automatically interrogated at presettimes of day to telemeter out accumulated data to a telephoniccommunicator or a full disclosure recorder. The communicator can beautomatically triggered to establish a telephonic communication link andtransmit the accumulated data to an office or clinic through a modem. Inthe '245 patent, telemetered data is downloaded to a larger capacity,external data recorder and is forwarded to a clinic using an auto-dialerand fax modem operating in a personal computer-basedprogrammer/interrogator. However, the '976 telemetry transceiver, '869communicator, and '245 programmer/interrogator are limited tofacilitating communication and transferal of downloaded patient data anddo not include an ability to automatically track, recognize, and analyzetrends in the data itself.

In addition, the uses of multiple sensors situated within a patient'sbody at multiple sites are disclosed in U.S. Pat. No. 5,040,536 ('536)and U.S. Pat. No. 5,987,352 ('352). In the '536 patent, an intravascularpressure posture detector includes at least two pressure sensorsimplanted in different places in the cardiovascular system, such thatdifferences in pressure with changes in posture are differentiallymeasurable. However, the physiological measurements are used locallywithin the device, or in conjunction with any implantable device, toeffect a therapeutic treatment. In the '352 patent, an event monitor caninclude additional sensors for monitoring and recording physiologicalsignals during arrhythmia and syncopal events. The recorded signals canbe used for diagnosis, research or therapeutic study, although nosystematic approach to analyzing these signals, particularly withrespect to peer and general population groups, is presented.

Thus, there is a need for a system and method for providing continuousretrieval, transferal, and automated analysis of retrieved medicaldevice information, such as telemetered signals, retrieved in generalfrom a broad class of implantable and external medical devices.Preferably, the automated analysis would include recognizing a trendindicating disease onset, progression, regression, and status quo anddetermining whether medical intervention is necessary.

There is a further need for a system and method that would allowconsideration of sets of collected measures, both actual and derived,from multiple device interrogations. These collected measures sets couldthen be compared and analyzed against short and long term periods ofobservation.

There is a further need for a system and method that would enable themeasures sets for an individual patient to be self-referenced andcross-referenced to similar or dissimilar patients and to the generalpatient population. Preferably, the historical collected measures setsof an individual patient could be compared and analyzed against those ofother patients in general or of a disease specific peer group inparticular.

SUMMARY OF THE INVENTION

The present invention provides a system and method for automatedcollection and analysis of patient information retrieved from animplantable medical device for remote patient care. The patient deviceinformation relates to individual measures recorded by and retrievedfrom implantable medical devices, such as IPGs and monitors. The patientdevice information is received on a regular, e.g., daily, basis as setsof collected measures which are stored along with other patient recordsin a database. The information can be analyzed in an automated fashionand feedback provided to the patient at any time and in any location.

An embodiment of the present invention is a system and method foranalyzing patient information for use in automated patient care. One ormore physiological measures relating to individual patient informationrecorded on a substantially continuous basis are retrieved from apatient care record. The physiological measures retrieved from one suchpatient care record are analyzed to determine a patient status. Eachphysiological measure is representative of at least one of measured andderived patient information.

A further embodiment is a system and method for collecting physiologicalmeasures for use in automated patient care. One or more physiologicalmeasures relating to individual patient information are obtained from amedical device having a sensor for monitoring and recording from ananatomical site at least one of directly and derivatively. Thephysiological measures are stored in patient care records.

A further embodiment is a system and method for providing tiered patientfeedback for use in automated patient care. Physiological measures areretrieved from one such patient care record are analyzed to determine apatient status. Each physiological measure is representative of at leastone of measured and derived patient information recorded on asubstantially continuous basis. Tiered feedback is provided to anindividual patient responsive to the patient status.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 2 is a block diagram showing the hardware components of the serversystem of the system of FIG. 1;

FIG. 3 is a block diagram showing the software modules of the serversystem of the system of FIG. 1;

FIG. 4 is a block diagram showing the analysis module of the serversystem of FIG. 3;

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored in the database of the system ofFIG. 1;

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database of the system ofFIG. 1;

FIG. 7 is a flow diagram showing a method for automated collection andanalysis of patient information retrieved from an implantable medicaldevice for remote patient care in accordance with the present invention;

FIG. 8 is a flow diagram showing a routine for analyzing collectedmeasures sets for use in the method of FIG. 7;

FIG. 9 is a flow diagram showing a routine for comparing siblingcollected measures sets for use in the routine of FIG. 8;

FIGS. 10A and 10B are flow diagrams showing a routine for comparing peercollected measures sets for use in the routine of FIG. 8; and

FIG. 11 is a flow diagram showing a routine for providing feedback foruse in the method of FIG. 7;

FIG. 12 is a block diagram showing a system for automated collection andanalysis of regularly retrieved patient information for remote patientcare in accordance with a further embodiment of the present invention;

FIG. 13 is a block diagram showing the analysis module of the serversystem of FIG. 12;

FIG. 14 is a database schema showing, by way of example, theorganization of a quality of life and symptom measures set record forcare of patients stored as part of a patient care record in the databaseof the system of FIG. 12;

FIG. 15 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database of the system ofFIG. 12;

FIG. 16 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records of FIG. 15;

FIGS. 17A–17B are flow diagrams showing a method for automatedcollection and analysis of regularly retrieved patient information forremote patient care in accordance with a further embodiment of thepresent invention; and

FIG. 18 is a flow diagram showing a routine for analyzing collectedmeasures sets for use in the method of FIGS. 17A–17B.

DETAILED DESCRIPTION

FIG. 1 is a block diagram showing a system 10 for automated collectionand analysis of patient information retrieved from an implantablemedical device for remote patient care in accordance with the presentinvention. A patient 11 is a recipient of an implantable medical device12, such as, by way of example, an IPG or a heart failure or eventmonitor, with a set of leads extending into his or her heart. Theimplantable medical device 12 includes circuitry for recording into ashort-term, volatile memory telemetered signals, which are stored as aset of collected measures for later retrieval.

For an exemplary cardiac implantable medical device, the telemeteredsignals non-exclusively present patient information relating to: atrialelectrical activity, ventricular electrical activity, time of day,activity level, cardiac output, oxygen level, cardiovascular pressuremeasures, the number and types of interventions made, and the relativesuccess of any interventions made on a per heartbeat or binned averagebasis, plus the status of the batteries and programmed settings.Examples of pacemakers suitable for use in the present invention includethe Discovery line of pacemakers, manufactured by Guidant Corporation,Indianapolis, Ind. Examples of ICDs suitable for use in the presentinvention include the Ventak line of ICDs, also manufactured by GuidantCorporation, Indianapolis, Ind.

In the described embodiment, the patient 11 has a cardiac implantablemedical device. However, a wide range of related implantable medicaldevices are used in other areas of medicine and a growing number ofthese devices are also capable of measuring and recording patientinformation for later retrieval. These implantable medical devicesinclude monitoring and therapeutic devices for use in metabolism,endocrinology, hematology, neurology, muscularology,gastro-intestinalogy, genital-urology, ocular, auditory, and similarmedical subspecialties. One skilled in the art would readily recognizethe applicability of the present invention to these related implantablemedical devices.

On a regular basis, the telemetered signals stored in the implantablemedical device 12 are retrieved. By way of example, a programmer 14 canbe used to retrieve the telemetered signals. However, any form ofprogrammer, interrogator, recorder, monitor, or telemetered signalstransceiver suitable for communicating with an implantable medicaldevice 12 could be used, as is known in the art. In addition, a personalcomputer or digital data processor could be interfaced to theimplantable medical device 12, either directly or via a telemeteredsignals transceiver configured to communicate with the implantablemedical device 12.

Using the programmer 14, a magnetized reed switch (not shown) within theimplantable medical device 12 closes in response to the placement of awand 13 over the location of the implantable medical device 12. Theprogrammer 14 communicates with the implantable medical device 12 via RFsignals exchanged through the wand 14. Programming or interrogatinginstructions are sent to the implantable medical device 12 and thestored telemetered signals are downloaded into the programmer 14. Oncedownloaded, the telemetered signals are sent via an internetwork 15,such as the Internet, to a server system 16 which periodically receivesand stores the telemetered signals in a database 17, as furtherdescribed below with reference to FIG. 2.

An example of a programmer 14 suitable for use in the present inventionis the Model 2901 Programmer Recorder Monitor, manufactured by GuidantCorporation, Indianapolis, Ind., which includes the capability to storeretrieved telemetered signals on a proprietary removable floppydiskette. The telemetered signals could later be electronicallytransferred using a personal computer or similar processing device tothe internetwork 15, as is known in the art.

Other alternate telemetered signals transfer means could also beemployed. For instance, the stored telemetered signals could beretrieved from the implantable medical device 12 and electronicallytransferred to the internetwork 15 using the combination of a remoteexternal programmer and analyzer and a remote telephonic communicator,such as described in U.S. Pat. No. 5,113,869, the disclosure of which isincorporated herein by reference. Similarly, the stored telemeteredsignals could be retrieved and remotely downloaded to the server system16 using a world-wide patient location and data telemetry system, suchas described in U.S. Pat. No. 5,752,976, the disclosure of which isincorporated herein by reference.

The received telemetered signals are analyzed by the server system 16,which generates a patient status indicator. The feedback is thenprovided back to the patient 11 through a variety of means. By way ofexample, the feedback can be sent as an electronic mail messagegenerated automatically by the server system 16 for transmission overthe internetwork 15. The electronic mail message is received by personalcomputer 18 (PC) situated for local access by the patient 11.Alternatively, the feedback can be sent through a telephone interfacedevice 19 as an automated voice mail message to a telephone 21 or as anautomated facsimile message to a facsimile machine 22, both alsosituated for local access by the patient 11. In addition to a personalcomputer 18, telephone 21, and facsimile machine 22, feedback could besent to other related devices, including a network computer, wirelesscomputer, personal data assistant, television, or digital dataprocessor. Preferably, the feedback is provided in a tiered fashion, asfurther described below with reference to FIG. 3.

FIG. 2 is a block diagram showing the hardware components of the serversystem 16 of the system 10 of FIG. 1. The server system 16 consists ofthree individual servers: network server 31, database server 34, andapplication server 35. These servers are interconnected via anintranetwork 33. In the described embodiment, the functionality of theserver system 16 is distributed among these three servers for efficiencyand processing speed, although the functionality could also be performedby a single server or cluster of servers. The network server 31 is theprimary interface of the server system 16 onto the internetwork 15. Thenetwork server 31 periodically receives the collected telemeteredsignals sent by remote implantable medical devices over the internetwork15. The network server 31 is interfaced to the internetwork 15 through arouter 32. To ensure reliable data exchange, the network server 31implements a TCP/IP protocol stack, although other forms of networkprotocol stacks are suitable.

The database server 34 organizes the patient care records in thedatabase 17 and provides storage of and access to information held inthose records. A high volume of data in the form of collected measuressets from individual patients is received. The database server 34 freesthe network server 31 from having to categorize and store the individualcollected measures sets in the appropriate patient care record.

The application server 35 operates management applications and performsdata analysis of the patient care records, as further described belowwith reference to FIG. 3. The application server 35 communicatesfeedback to the individual patients either through electronic mail sentback over the internetwork 15 via the network server 31 or as automatedvoice mail or facsimile messages through the telephone interface device19.

The server system 16 also includes a plurality of individualworkstations 36 (WS) interconnected to the intranetwork 33, some ofwhich can include peripheral devices, such as a printer 37. Theworkstations 36 are for use by the data management and programmingstaff, nursing staff, office staff, and other consultants and authorizedpersonnel.

The database 17 consists of a high-capacity storage medium configured tostore individual patient care records and related health careinformation. Preferably, the database 17 is configured as a set ofhigh-speed, high capacity hard drives, such as organized into aRedundant Array of Inexpensive Disks (RAID) volume. However, any form ofvolatile storage, non-volatile storage, removable storage, fixedstorage, random access storage, sequential access storage, permanentstorage, erasable storage, and the like would be equally suitable. Theorganization of the database 17 is further described below withreference to FIG. 3.

The individual servers and workstations are general purpose, programmeddigital computing devices consisting of a central processing unit (CPU),random access memory (RAM), non-volatile secondary storage, such as ahard drive or CD ROM drive, network interfaces, and peripheral devices,including user interfacing means, such as a keyboard and display.Program code, including software programs, and data are loaded into theRAM for execution and processing by the CPU and results are generatedfor display, output, transmittal, or storage. In the describedembodiment, the individual servers are Intel Pentium-based serversystems, such as available from Dell Computers, Austin, Tex., or CompaqComputers, Houston, Tex. Each system is preferably equipped with 128 MBRAM, 100 GB hard drive capacity, data backup facilities, and relatedhardware for interconnection to the intranetwork 33 and internetwork 15.In addition, the workstations 36 are also Intel Pentium-based personalcomputer or workstation systems, also available from Dell Computers,Austin, Tex., or Compaq Computers, Houston, Tex. Each workstation ispreferably equipped with 64 MB RAM, 10 GB hard drive capacity, andrelated hardware for interconnection to the intranetwork 33. Other typesof server and workstation systems, including personal computers,minicomputers, mainframe computers, supercomputers, parallel computers,workstations, digital data processors and the like would be equallysuitable, as is known in the art.

The telemetered signals are communicated over an internetwork 15, suchas the Internet. However, any type of electronic communications linkcould be used, including an intranetwork link, serial link, datatelephone link, satellite link, radio-frequency link, infrared link,fiber optic link, coaxial cable link, television link, and the like, asis known in the art. Also, the network server 31 is interfaced to theinternetwork 15 using a T-1 network router 32, such as manufactured byCisco Systems, Inc., San Jose, Calif. However, any type of interfacingdevice suitable for interconnecting a server to a network could be used,including a data modem, cable modem, network interface, serialconnection, data port, hub, frame relay, digital PBX, and the like, asis known in the art.

FIG. 3 is a block diagram showing the software modules of the serversystem 16 of the system 10 of FIG. 1. Each module is a computer programwritten as source code in a conventional programming language, such asthe C or Java programming languages, and is presented for execution bythe CPU as object or byte code, as is known in the arts. The variousimplementations of the source code and object and byte codes can be heldon a computer-readable storage medium or embodied on a transmissionmedium in a carrier wave. There are three basic software modules, whichfunctionally define the primary operations performed by the serversystem 16: database module 51, analysis module 53, and feedback module55. In the described embodiment, these modules are executed in adistributed computing environment, although a single server or a clusterof servers could also perform the functionality of the modules. Themodule functions are further described below in more detail beginningwith reference to FIG. 7.

For each patient being provided remote patient care, the server system16 periodically receives a collected measures set 50 which is forwardedto the database module 51 for processing. The database module 51organizes the individual patent care records stored in the database 52and provides the facilities for efficiently storing and accessing thecollected measures sets 50 and patient data maintained in those records.An exemplary database schema for use in storing collected measures sets50 in a patient care record is described below, by way of example, withreference to FIG. 5. The database server 34 (shown in FIG. 2) performsthe functionality of the database module 51. Any type of databaseorganization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

The analysis module 53 analyzes the collected measures sets 50 stored inthe patient care records in the database 52. The analysis module 53makes an automated determination of patient wellness in the form of apatient status indicator 54. Collected measures sets 50 are periodicallyreceived from implantable medical devices and maintained by the databasemodule 51 in the database 52. Through the use of this collectedinformation, the analysis module 53 can continuously follow the medicalwell being of a patient and can recognize any trends in the collectedinformation that might warrant medical intervention. The analysis module53 compares individual measures and derived measures obtained from boththe care records for the individual patient and the care records for adisease specific group of patients or the patient population in general.The analytic operations performed by the analysis module 53 are furtherdescribed below with reference to FIG. 4. The application server 35(shown in FIG. 2) performs the functionality of the analysis module 53.

The feedback module 55 provides automated feedback to the individualpatient based, in part, on the patient status indicator 54. As describedabove, the feedback could be by electronic mail or by automated voicemail or facsimile. Preferably, the feedback is provided in a tieredmanner. In the described embodiment, four levels of automated feedbackare provided. At a first level, an interpretation of the patient statusindicator 54 is provided. At a second level, a notification of potentialmedical concern based on the patient status indicator 54 is provided.This feedback level could also be coupled with human contact byspecially trained technicians or medical personnel. At a third level,the notification of potential medical concern is forwarded to medicalpractitioners located in the patient's geographic area. Finally, at afourth level, a set of reprogramming instructions based on the patientstatus indicator 54 could be transmitted directly to the implantablemedical device to modify the programming instructions contained therein.As is customary in the medical arts, the basic tiered feedback schemewould be modified in the event of bona fide medical emergency. Theapplication server 35 (shown in FIG. 2) performs the functionality ofthe feedback module 55.

FIG. 4 is a block diagram showing the analysis module 53 of the serversystem 16 of FIG. 3. The analysis module 53 contains two functionalsubmodules: comparison module 62 and derivation module 63. The purposeof the comparison module 62 is to compare two or more individualmeasures, either collected or derived. The purpose of the derivationmodule 63 is to determine a derived measure based on one or morecollected measures which is then used by the comparison module 62. Forinstance, a new and improved indicator of impending heart failure couldbe derived based on the exemplary cardiac collected measures setdescribed with reference to FIG. 5. The analysis module 53 can operateeither in a batch mode of operation wherein patient status indicatorsare generated for a set of individual patients or in a dynamic modewherein a patient status indicator is generated on the fly for anindividual patient.

The comparison module 62 receives as inputs from the database 17 twoinput sets functionally defined as peer collected measures sets 60 andsibling collected measures sets 61, although in practice, the collectedmeasures sets are stored on a per sampling basis. Peer collectedmeasures sets 60 contain individual collected measures sets that allrelate to the same type of patient information, for instance, atrialelectrical activity, but which have been periodically collected overtime. Sibling collected measures sets 61 contain individual collectedmeasures sets that relate to different types of patient information, butwhich may have been collected at the same time or different times. Inpractice, the collected measures sets are not separately stored as“peer” and “sibling” measures. Rather, each individual patient carerecord stores multiple sets of sibling collected measures. Thedistinction between peer collected measures sets 60 and siblingcollected measures sets 61 is further described below with reference toFIG. 6.

The derivation module 63 determines derived measures sets 64 on anas-needed basis in response to requests from the comparison module 62.The derived measures 64 are determined by performing linear andnon-linear mathematical operations on selected peer measures 60 andsibling measures 61, as is known in the art.

FIG. 5 is a database schema showing, by way of example, the organizationof a cardiac patient care record stored 70 in the database 17 of thesystem 10 of FIG. 1. Only the information pertaining to collectedmeasures sets are shown. Each patient care record would also containnormal identifying and treatment profile information, as well as medicalhistory and other pertinent data (not shown). Each patient care recordstores a multitude of collected measures sets for an individual patient.Each individual set represents a recorded snapshot of telemeteredsignals data which was recorded, for instance, per heartbeat or binnedaverage basis by the implantable medical device 12. For example, for acardiac patient, the following information would be recorded as acollected measures set: atrial electrical activity 71, ventricularelectrical activity 72, time of day 73, activity level 74, cardiacoutput 75, oxygen level 76, cardiovascular pressure measures 77,pulmonary measures 78, interventions made by the implantable medicaldevice 78, and the relative success of any interventions made 80. Inaddition, the implantable medical device 12 would also communicatedevice specific information, including battery status 81 and programsettings 82. Other types of collected measures are possible. Inaddition, a well-documented set of derived measures can be determinedbased on the collected measures, as is known in the art.

FIG. 6 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database 17 of the system 10of FIG. 1. Three patient care records are shown for Patient 1, Patient2, and Patient 3. For each patient, three sets of measures are shown, X,Y, and Z. The measures are organized into sets with Set 0 representingsibling measures made at a reference time t=0. Similarly, Set n-2, Setn-1 and Set n each represent sibling measures made at later referencetimes t=n-2, t=n-1 and t=n, respectively.

For a given patient, for instance, Patient 1, all measures representingthe same type of patient information, such as measure X, are peermeasures. These are measures, which are monitored over time in adisease-matched peer group. All measures representing different types ofpatient information, such as measures X, Y, and Z, are sibling measures.These are measures which are also measured over time, but which mighthave medically significant meaning when compared to each other within asingle set. Each of the measures, X, Y, and Z, could be either collectedor derived measures.

The analysis module 53 (shown in FIG. 4) performs two basic forms ofcomparison. First, individual measures for a given patient can becompared to other individual measures for that same patient. Thesecomparisons might be peer-to-peer measures projected over time, forinstance, X_(n), X_(n-1), X_(n-2), . . . X₀, or sibling-to-siblingmeasures for a single snapshot, for instance, X_(n), Y_(n), and Z_(n),or projected over time, for instance, X_(n), Y_(n), Z_(n), X_(n-1),Y_(n-1), Z_(n-1), X_(n-2), Y_(n-2), Z_(n-2), . . . X₀, Y₀, Z₀. Second,individual measures for a given patient can be compared to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. Again, these comparisons might be peer-to-peer measuresprojected over time, for instance, X_(n), X_(n′), X_(n″), X_(n-1),X_(n-1′), X_(n-1″), X_(n-2), X_(n-2′), X_(n-2″) . . . X₀, X_(0′),X_(0″), or comparing the individual patient's measures to an averagefrom the group. Similarly, these comparisons might be sibling-to-siblingmeasures for single snapshots, for instance, X_(n), X_(n′), X_(n″),Y_(n), Y_(n′), Y_(n″), and Z_(n), Z_(n′), Z_(n″), or projected overtime, for instance, X_(n), X_(n′), X_(n″), Y_(n), Y_(n′), Y_(n″), Z_(n),Z_(n′), Z_(n″), X_(n-1), X_(n-1′), X_(n-1″), Y_(n-1), Y_(n-1′),Y_(n-1″), Z_(n-1), Z_(n-1′), Z_(n-1″), X_(n-2), X_(n-2′), X_(n-2″),Y_(n-2), Y_(n-2′), Y_(n-2″), Z_(n-2), Z_(n-2′), Z_(n-2″) . . . X₀,X_(0′), X_(0″), Y₀, Y_(0′), Y_(0″), and Z₀, Z_(0′), Z_(0″). Other formsof comparisons are feasible.

FIG. 7 is a flow diagram showing a method 90 for automated collectionand analysis of patient information retrieved from an implantablemedical device 12 for remote patient care in accordance with the presentinvention. The method 90 is implemented as a conventional computerprogram for execution by the server system 16 (shown in FIG. 1). As apreparatory step, the patient care records are organized in the database17 with a unique patient care record assigned to each individual patient(block 91). Next, the collected measures sets for an individual patientare retrieved from the implantable medical device 12 (block 92) using aprogrammer, interrogator, telemetered signals transceiver, and the like.The retrieved collected measures sets are sent, on a substantiallyregular basis, over the internetwork 15 or similar communications link(block 93) and periodically received by the server system 16 (block 94).The collected measures sets are stored into the patient care record inthe database 17 for that individual patient (block 95). One or more ofthe collected measures sets for that patient are analyzed (block 96), asfurther described below with reference to FIG. 8. Finally, feedbackbased on the analysis is sent to that patient over the internetwork 15as an email message, via telephone line as an automated voice mail orfacsimile message, or by similar feedback communications link (block97), as further described below with reference to FIG. 11.

FIG. 8 is a flow diagram showing the routine for analyzing collectedmeasures sets 96 for use in the method of FIG. 7. The purpose of thisroutine is to make a determination of general patient wellness based oncomparisons and heuristic trends analyses of the measures, bothcollected and derived, in the patient care records in the database 17. Afirst collected measures set is selected from a patient care record inthe database 17 (block 100). If the measures comparison is to be made toother measures originating from the patient care record for the sameindividual patient (block 101), a second collected measures set isselected from that patient care record (block 102). Otherwise, a groupmeasures comparison is being made (block 101) and a second collectedmeasures set is selected from another patient care record in thedatabase 17 (block 103). Note the second collected measures set couldalso contain averaged measures for a group of disease specific patientsor for the patient population in general.

Next, if a sibling measures comparison is to be made (block 104), aroutine for comparing sibling collected measures sets is performed(block 105), as further described below with reference to FIG. 9.Similarly, if a peer measures comparison is to be made (block 106), aroutine for comparing sibling collected measures sets is performed(block 107), as further described below with reference to FIGS. 10A and10B.

Finally, a patient status indicator is generated (block 108). By way ofexample, cardiac output could ordinarily be approximately 5.0 liters perminute with a standard deviation of ±1.0. An actionable medicalphenomenon could occur when the cardiac output of a patient is ±3.0–4.0standard deviations out of the norm. A comparison of the cardiac outputmeasures 75 (shown in FIG. 5) for an individual patient against previouscardiac output measures 75 would establish the presence of any type ofdownward health trend as to the particular patient. A comparison of thecardiac output measures 75 of the particular patient to the cardiacoutput measures 75 of a group of patients would establish whether thepatient is trending out of the norm. From this type of analysis, theanalysis module 53 generates a patient status indicator 54 and othermetrics of patient wellness, as is known in the art.

FIG. 9 is a flow diagram showing the routine for comparing siblingcollected measures sets 105 for use in the routine of FIG. 8. Siblingmeasures originate from the patient care records for an individualpatient. The purpose of this routine is either to compare siblingderived measures to sibling derived measures (blocks 111–113) or siblingcollected measures to sibling collected measures (blocks 115–117). Thus,if derived measures are being compared (block 110), measures areselected from each collected measures set (block 111). First and secondderived measures are derived from the selected measures (block 112)using the derivation module 63 (shown in FIG. 4). The first and secondderived measures are then compared (block 113) using the comparisonmodule 62 (also shown in FIG. 4). The steps of selecting, determining,and comparing (blocks 111–113) are repeated until no further comparisonsare required (block 114), whereupon the routine returns.

If collected measures are being compared (block 110), measures areselected from each collected measures set (block 115). The first andsecond collected measures are then compared (block 116) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting andcomparing (blocks 115–116) are repeated until no further comparisons arerequired (block 117), whereupon the routine returns.

FIGS. 10A and 10B are a flow diagram showing the routine for comparingpeer collected measures sets 107 for use in the routine of FIG. 8. Peermeasures originate from patient care records for different patients,including groups of disease specific patients or the patient populationin general. The purpose of this routine is to compare peer derivedmeasures to peer derived measures (blocks 122–125), peer derivedmeasures to peer collected measures (blocks 126–129), peer collectedmeasures to peer derived measures (block 131–134), or peer collectedmeasures to peer collected measures (blocks 135–137). Thus, if the firstmeasure being compared is a derived measure (block 120) and the secondmeasure being compared is also a derived measure (block 121), measuresare selected from each collected measures set (block 122). First andsecond derived measures are derived from the selected measures (block123) using the derivation module 63 (shown in FIG. 4). The first andsecond derived measures are then compared (block 124) using thecomparison module 62 (also shown in FIG. 4). The steps of selecting,determining, and comparing (blocks 122–124) are repeated until nofurther comparisons are required (block 115), whereupon the routinereturns.

If the first measure being compared is a derived measure (block 120) butthe second measure being compared is a collected measure (block 121), afirst measure is selected from the first collected measures set (block126). A first derived measure is derived from the first selected measure(block 127) using the derivation module 63 (shown in FIG. 4). The firstderived and second collected measures are then compared (block 128)using the comparison module 62 (also shown in FIG. 4). The steps ofselecting, determining, and comparing (blocks 126–128) are repeateduntil no further comparisons are required (block 129), whereupon theroutine returns.

If the first measure being compared is a collected measure (block 120)but the second measure being compared is a derived measure (block 130),a second measure is selected from the second collected measures set(block 131). A second derived measure is derived from the secondselected measure (block 132) using the derivation module 63 (shown inFIG. 4). The first collected and second derived measures are thencompared (block 133) using the comparison module 62 (also shown in FIG.4). The steps of selecting, determining, and comparing (blocks 131–133)are repeated until no further comparisons are required (block 134),whereupon the routine returns.

If the first measure being compared is a collected measure (block 120)and the second measure being compared is also a collected measure (block130), measures are selected from each collected measures set (block135). The first and second collected measures are then compared (block136) using the comparison module 62 (also shown in FIG. 4). The steps ofselecting and comparing (blocks 135–136) are repeated until no furthercomparisons are required (block 137), whereupon the routine returns.

FIG. 11 is a flow diagram showing the routine for providing feedback 97for use in the method of FIG. 7. The purpose of this routine is toprovide tiered feedback based on the patient status indicator. Fourlevels of feedback are provided with increasing levels of patientinvolvement and medical care intervention. At a first level (block 150),an interpretation of the patient status indicator 54, preferably phrasedin lay terminology, and related health care information is sent to theindividual patient (block 151) using the feedback module 55 (shown inFIG. 3). At a second level (block 152), a notification of potentialmedical concern, based on the analysis and heuristic trends analysis, issent to the individual patient (block 153) using the feedback module 55.At a third level (block 154), the notification of potential medicalconcern is forwarded to the physician responsible for the individualpatient or similar health care professionals (block 155) using thefeedback module 55. Finally, at a fourth level (block 156),reprogramming instructions are sent to the implantable medical device 12(block 157) using the feedback module 55.

Therefore, through the use of the collected measures sets, the presentinvention makes possible immediate access to expert medical care at anytime and in any place. For example, after establishing and registeringfor each patient an appropriate baseline set of measures, the databaseserver could contain a virtually up-to-date patient history, which isavailable to medical providers for the remote diagnosis and preventionof serious illness regardless of the relative location of the patient ortime of day.

Moreover, the gathering and storage of multiple sets of critical patientinformation obtained on a routine basis makes possible treatmentmethodologies based on an algorithmic analysis of the collected datasets. Each successive introduction of a new collected measures set intothe database server would help to continually improve the accuracy andeffectiveness of the algorithms used. In addition, the present inventionpotentially enables the detection, prevention, and cure of previouslyunknown forms of disorders based on a trends analysis and by across-referencing approach to create continuously improving peer-groupreference databases.

Finally, the present invention makes possible the provision of tieredpatient feedback based on the automated analysis of the collectedmeasures sets. This type of feedback system is suitable for use in, forexample, a subscription based health care service. At a basic level,informational feedback can be provided by way of a simple interpretationof the collected data. The feedback could be built up to provide agradated response to the patient, for example, to notify the patientthat he or she is trending into a potential trouble zone. Humaninteraction could be introduced, both by remotely situated and localmedical practitioners. Finally, the feedback could include directinterventive measures, such as remotely reprogramming a patient's IPG.

FIG. 12 is a block diagram showing a system for automated collection andanalysis of regularly retrieved patient information for remote patientcare 200 in accordance with a further embodiment of the presentinvention. The system 200 provides remote patient care in a mannersimilar to the system 10 of FIG. 1, but with additional functionalityfor diagnosing and monitoring multiple sites within a patient's bodyusing a variety of patient sensors for diagnosing one or more disorder.The patient 201 can be the recipient of an implantable medical device202, as described above, or have an external medical device 203attached, such as a Holter monitor-like device for monitoringelectrocardiograms. In addition, one or more sites in or around thepatient's body can be monitored using multiple sensors 204 a, 204 b,such as described in U.S. Pat. Nos. 4,987,897; 5,040,536; 5,113,859; and5,987,352, the disclosures of which are incorporated herein byreference. Other types of devices with physiological measure sensors,both heterogeneous and homogenous, could be used, either within the samedevice or working in conjunction with each other, as is known in theart.

As part of the system 200, the database 17 stores patient care records205 for each individual patient to whom remote patient care is beingprovided. Each patient care record 205 contains normal patientidentification and treatment profile information, as well as medicalhistory, medications taken, height and weight, and other pertinent data(not shown). The patient care records 205 consist primarily ofmonitoring sets 206 storing device and derived measures (D&DM) sets 207and quality of life and symptom measures (QOLM) sets 208 recorded anddetermined thereafter on a regular, continuous basis. The organizationof the device and derived measures sets 205 for an exemplary cardiacpatient care record is described above with reference to FIG. 5. Theorganization of the quality of life and symptom measures sets 208 isfurther described below with reference to FIG. 14.

Optionally, the patient care records 205 can further include a referencebaseline 209 storing a special set of device and derived referencemeasures sets 210 and quality of life and symptom measures sets 211recorded and determined during an initial observation period, such asdescribed in the related, commonly-owned U.S. patent application Ser.No. 09/476,601, pending, filed Dec. 31, 1999, the disclosure of which isincorporated herein by reference. Other forms of database organizationare feasible.

Finally, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider212 using feedback means similar to that used to notify the patient. Asdescribed above, the feedback could be by electronic mail or byautomated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related,commonly-owned U.S. Pat. application Ser. No. 09/476,600, pending, filedDec. 31, 1999, the disclosure of which is incorporated herein byreference.

FIG. 13 is a block diagram showing the analysis module 53 of the serversystem 16 of FIG. 12. The peer collected measures sets 60 and siblingcollected measures sets 61 can be organized into site specific groupingsbased on the sensor from which they originate, that is, implantablemedical device 202, external medical device 203, or multiple sensors 204a, 204 b. The functionality of the analysis module 53 is augmented toiterate through a plurality of site specific measures sets 215 and oneor more disorders.

As an adjunct to remote patient care through the monitoring of measuredphysiological data via implantable medical device 202, external medicaldevice 203 and multiple sensors 204 a, 204 b, quality of life andsymptom measures sets 208 can also be stored in the database 17 as partof the monitoring sets 206. A quality of life measure is asemi-quantitative self-assessment of an individual patient's physicaland emotional well-being and a record of symptoms, such as provided bythe Duke Activities Status Indicator. These scoring systems can beprovided for use by the patient 11 on the personal computer 18 (shown inFIG. 1) to record his or her quality of life scores for both initial andperiodic download to the server system 16. FIG. 14 is a database schemashowing, by way of example, the organization of a quality of life andsymptom measures set record 220 for care of patients stored as part of apatient care record 205 in the database 17 of the system 200 of FIG. 12.The following exemplary information is recorded for a patient: overallhealth wellness 221, psychological state 222, chest discomfort 223,location of chest discomfort 224, palpitations 225, shortness of breath226, exercise tolerance 227, cough 228, sputum production 229, sputumcolor 230, energy level 231, syncope 232, near syncope 233, nausea 234,diaphoresis 235, time of day 91, and other quality of life and symptommeasures as would be known to one skilled in the art.

Other types of quality of life and symptom measures are possible, suchas those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452–454, W. B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

The patient may also add non-device quantitative measures, such as thesix-minute walk distance, as complementary data to the device andderived measures sets 207 and the symptoms during the six-minute walk toquality of life and symptom measures sets 208.

FIG. 15 is a record view showing, by way of example, a set of partialcardiac patient care records stored in the database 17 of the system 200of FIG. 12. Three patient care records are again shown for Patient 1,Patient 2, and Patient 3 with each of these records containing sitespecific measures sets 215, grouped as follows. First, the patient carerecord for Patient 1 includes three site specific measures sets A, B andC, corresponding to three sites on Patient 1's body. Similarly, thepatient care record for Patient 2 includes two site specific measuressets A and B, corresponding to two sites, both of which are in the samerelative positions on Patient 2's body as the sites for Patient 1.Finally, the patient care record for Patient 3 includes two sitespecific measures sets A and D, also corresponding to two medical devicesensors, only one of which, Site A, is in the same relative position asSite A for Patient 1 and Patient 2.

The analysis module 53 (shown in FIG. 13) performs two further forms ofcomparison in addition to comparing the individual measures for a givenpatient to other individual measures for that same patient or to otherindividual measures for a group of other patients sharing the samedisease-specific characteristics or to the patient population ingeneral. First, the individual measures corresponding to each body sitefor an individual patient can be compared to other individual measuresfor that same patient, a peer group or a general patient population.Again, these comparisons might be peer-to-peer measures projected overtime, for instance, comparing measures for each site, A, B and C, forPatient 1, X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) , X_(n-1) _(A) ,X_(n-1′) _(A) , X_(n-1″) _(A) , X_(n-2) _(A) , X_(n-2) _(A, X) _(n-2)_(A) . . . X₀ _(A) , X₀ _(A, X) _(0″) _(A) ; X_(n) _(B) , X_(n) _(B, X)_(n″) _(B) , X_(n-1) _(B) , X_(n-1) _(B, X) _(n-1″) _(B) , X_(n-2) _(B), X_(n-2) _(B, X) _(n-2″) _(B) . . . X₀ _(B) , X_(0′) _(B) , X_(0″) _(B); X_(n) _(C) , X_(n′) _(C) , X_(n) _(C) ″, X_(n-) _(C) , X_(n-1′) _(C) ,X_(n-1″) _(C) , X_(n-2) _(C) , X_(n-2′) _(C) , X_(n-2″) _(C) . . . X₀_(C) , X_(0′) _(C) , X_(0″) _(C) ; comparing comparable measures forSite A for the three patients, X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) ,X_(n-1) _(A) , X_(n-1′) _(A) , X_(n-1″) _(A) , X_(n-2) _(A) , X_(n-2′)_(A) , X_(n-2″) _(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″) _(A) ; orcomparing the individual patient's measures to an average from thegroup. Similarly, these comparisons might be sibling-to-sibling measuresfor single snapshots, for instance, comparing comparable measures forSite A for the three patients, X_(n) _(A) , X_(n′) _(A) , X_(n″) _(A) ,Y_(n) _(A) , Y_(n′) _(A) , Y_(n″) _(A) , and Z_(n) _(A) , Z_(n′) _(A) ,Z_(n″) _(A) , or comparing those same comparable measures for Site Aprojected over time, for instance, X_(n) _(A) , X_(n′) _(A) , X_(n″)_(A) , Y_(n) _(A) , Y_(n″) _(A) , Y_(n) _(A) , Z_(n) _(A) , Z_(n′) _(A), Z_(n″) _(A) , X_(n-1) _(A) , X_(n-1′) _(A) , X_(n-1″) _(A) , Y_(n-1)_(A) , Y_(n-1′) _(A) , Y_(n-1″) _(A) , Z_(n-1) _(A) , Z_(n-1′) _(A) ,Z_(n-1″) _(A) , X_(n-2) _(A) , X_(n-2′) _(A) , X_(n-2″) _(A) , Y_(n-2)_(A) , Y_(n-2′) _(A) , Y_(n-2″) _(A) , Z_(n-2) _(A) , Z_(n-2′) _(A) ,Z_(n-2″) _(A) . . . X₀ _(A) , X_(0′) _(A) , X_(0″) _(A) , Y₀ _(A) ,Y_(0′) _(A) , Y_(0″) _(A) , and Z₀ _(A) , Z_(0′) _(A) , Z_(0″) _(A) .Other forms of site-specific comparisons, including comparisons betweenindividual measures from non-comparable sites between patients, arefeasible.

Second the individual measures can be compared on a disorder specificbasis. The individual measures stored in each cardiac patient record canbe logically grouped into measures relating to specific disorders anddiseases, for instance, congestive heart failure, myocardial infarction,respiratory distress, and atrial fibrillation. The foregoing comparisonoperations performed by the analysis module 53 are further describedbelow with reference to FIGS. 17A–17B.

FIG. 16 is a Venn diagram showing, by way of example, peer group overlapbetween the partial patient care records 205 of FIG. 15. Each patientcare record 205 includes characteristics data 250, 251, 252, includingpersonal traits, demographics, medical history, and related personaldata, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 250 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40–45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 251 for patient 2might include identical personal traits, thereby resulting in partialoverlap 253 of characteristics data 250 and 251. Similar characteristicsoverlap 254, 255, 256 can exist between each respective patient. Theoverall patient population 257 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 256 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

FIGS. 17A–17B are flow diagrams showing a method for automatedcollection and analysis of regularly retrieved patient information forremote patient care 260 in accordance with a further embodiment of thepresent invention. As with the method 90 of FIG. 7, this method is alsoimplemented as a conventional computer program and performs the same setof steps as described with reference to FIG. 7 with the followingadditional functionality. As before, the patient care records areorganized in the database 17 with a unique patient care record assignedto each individual patient (block 261). Next, the individual measuresfor each site are iteratively obtained in a first processing loop(blocks 262–267) and each disorder is iteratively analyzed in a secondprocessing loop (blocks 268–270). Other forms of flow control arefeasible, including recursive processing.

During each iteration of the first processing loop (blocks 262–267), thecollected measures sets for an individual patient are retrieved from themedical device or sensor located at the current site (block 263) using aprogrammer, interrogator, telemetered signals transceiver, and the like.The retrieved collected measures sets are sent, on a substantiallyregular basis, over the internetwork 15 or similar communications link(block 264) and periodically received by the server system 16 (block265). The collected measures sets are stored into the patient carerecord 205 in the database 17 for that individual patient (block 266).

During each iteration of the second processing loop (blocks 268–270),one or more of the collected measures sets for that patient are analyzedfor the current disorder (block 269), as further described below withreference to FIG. 18. Finally, feedback based on the analysis is sent tothat patient over the internetwork 15 as an email message, via telephoneline as an automated voice mail or facsimile message, or by similarfeedback communications link (block 97), as further described above withreference to FIG. 11.

FIG. 18 is a flow diagram showing a routine for analyzing collectedmeasures sets 270 for use in the method 260 of FIGS. 17A–17B. Thepurpose of this routine is to make a determination of general patientwellness based on comparisons and heuristic trends analyses of thedevice and derived measures and quality of life and symptom measures inthe patient care records 205 in the database 17. A first collectedmeasures set is selected from a patient care record in the database 17(block 290). The selected measures set can either be compared to othermeasures originating from the patient care record for the sameindividual patient or to measures from a peer group of disease specificpatients or for the patient population in general (block 291). If thefirst collected measures set is being compared within an individualpatient care record (block 291), the selected measures set can either becompared to measures from the same site or from another site (block292). If from the same site (block 292), a second collected measures setis selected for the current site from that patient care record (block293). Otherwise, a second collected measures set is selected for anothersite from that patient care record (block 294). Similarly, if the firstcollected measures set is being compared within a group (block 291), theselected measures set can either be compared to measures from the samecomparable site or from another site (block 295). If from the samecomparable site (block 295), a second collected measures set is selectedfor a comparable site from another patient care record (block 296).Otherwise, a second collected measures set is selected for another sitefrom another patient care record (block 297). Note the second collectedmeasures set could also contain averaged measures for a group of diseasespecific patients or for the patient population in general.

Next, if a sibling measures comparison is to be made (block 298), theroutine for comparing sibling collected measures sets is performed(block 105), as further described above with reference to FIG. 9.Similarly, if a peer measures comparison is to be made (block 299), theroutine for comparing sibling collected measures sets is performed(block 107), as further described above with reference to FIGS. 10A and10B.

Finally, a patient status indicator is generated (block 300), asdescribed above with reference to FIG. 8. In addition, the measures setscan be further evaluated and matched to diagnose specific medicaldisorders, such as congestive heart failure, myocardial infarction,respiratory distress, and atrial fibrillation, as described in related,commonly-owned U.S. patent application Ser. No. 09/441,623, pending,filed Nov. 16, 1999; Ser. No. 09/441,612, pending, filed Nov. 16, 1999;Ser. No. 09/442,125, pending, filed Nov. 16, 1999, and Ser. No.09/441,613, pending, filed Nov. 16, 1999, the disclosures of which areincorporated herein by reference. In addition, multiplenear-simultaneous disorders can be ordered and prioritized as part ofthe patient status indicator as described in the related, commonly-ownedU.S. patent application Ser. No. 09/441,405, pending, filed Nov. 16,1999, the disclosure of which is incorporated herein by reference.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

1. A system for analyzing patient information for use in automatedpatient care, comprising: a database storing patient care records witheach record containing one or more physiological measures regularlyrecorded by an implantable medical device and relating to individualpatient information recorded on a substantially continuous basis andduring an initial observation period; and an analysis module analyzingthe physiological measures, each of the physiological measures beingrepresentative of at least one of measured and derived patientinformation, retrieved from one such patient care record to determine apatient status and determining one or more reference measures, each ofthe reference measures representative of at least one of measured andderived patient information, from the physiological measures retrievedfrom one such patient care record and storing the reference measuresinto the one such patient care record indicating a reference baselinepatient status.
 2. A system according to claim 1, further comprising: amedical device having a sensor for monitoring and recording thephysiological measures from an anatomical site at least one of directlyand derivatively.
 3. A system according to claim 2, further comprising:at least one further sensor monitoring and recording the physiologicalmeasures from an anatomical site unique from the anatomical sitemonitored by any other such sensor; and the analysis module determiningthe patient status by comparing physiological measures retrieved fromone such patient care record for a plurality of sensors.
 4. A systemaccording to claim 1, further comprising: the database storing thepatient care records with each of the patient care records containingthe physiological measures from sensors monitoring the one or moreanatomical sites within the individual patient; and the analysis moduleanalyzing the physiological measures from the one such patient carerecord relative to the anatomical sites.
 5. A system according to claim4, wherein the physiological measures monitor the same relative site. 6.A system according to claim 4, wherein the physiological measuresmonitor different relative sites.
 7. A system according to claim 4,wherein the physiological measures monitor relate to the same type ofpatient information.
 8. A system according to claim 4, wherein thephysiological measures monitor relate to different types of patientinformation.
 9. A system according to claim 1, further comprising: thedatabase storing the patient care records with each of the patient carerecords containing one or more quality of life measures relating tonormalized spoken patient self-assessment indicators, each such qualityof life measure recorded substantially contemporaneous to thephysiological measures; and the analysis module analyzing thephysiological measures and the contemporaneously recorded quality oflife measures retrieved from one such patient care record to determinethe patient status.
 10. A method for analyzing patient information foruse in automated patient care, comprising: retrieving from a patientcare record one or more physiological measures regularly recorded by animplantable medical device and relating to individual patientinformation recorded on a substantially continuous basis and during aninitial observation period; analyzing the physiological measures, eachof the physiological measures being representative of at least one ofmeasured and derived patient information, retrieved from one suchpatient care record to determine a patient status; determining one ormore reference measures, each of the reference measures representativeof at least one of measured and derived patient information, from thephysiological measures retrieved from one such patient care record; andstoring the reference measures into the one such patient care recordindicating a reference baseline patient status.
 11. A method accordingto claim 10, further comprising: obtaining the physiological measuresfrom a medical device having a sensor for monitoring the physiologicalmeasures from an anatomical site at least one of directly andderivatively.
 12. A method according to claim 11, further comprising:obtaining the physiological measures monitored by at least one furthersensor monitoring the physiological measures from an anatomical siteunique from the anatomical site monitored by any other such sensor; anddetermining the patient status by comparing physiological measuresretrieved from one such patient care record for a plurality of sensors.13. A method according to claim 10, further comprising: receiving thephysiological measures from sensors monitoring the one or moreanatomical sites within the individual patient; and analyzing thephysiological measures from the one such patient care record relative tothe anatomical sites.
 14. A method according to claim 13, wherein thephysiological measures monitor the same relative site.
 15. A methodaccording to claim 13, wherein the physiological measures monitordifferent relative sites.
 16. A method according to claim 13, whereinthe physiological measures monitor relate to the same type of patientinformation.
 17. A method according to claim 13, wherein thephysiological measures monitor relate to different types of patientinformation.
 18. A method according to claim 10, further comprising:retrieving one or more quality of life measures relating to normalizedspoken patient self-assessment indicators from a patient care record,each such quality of life measure recorded substantially contemporaneousto the physiological measures; and analyzing the physiological measuresand the contemporaneously recorded quality of life measures retrievedfrom one such patient care record to determine the patient status.
 19. Acomputer-readable storage medium for a device holding code forperforming the method according to claims 11, 13, 18, and 10.